The Advanced Data mining and Machine learning System, or short ADAMS, is a novel, flexible workflow engine aimed at quickly building and maintaining real-world, complex knowledge workflows. Instead of placing operators on a canvas and manually connecting them, a tree structure and flow control operators determine how the data is being processed, e.g., sequentially or in parallel. This approach allows the rapid development and easy maintenance of large workflows, consisting of hundreds or even thousands of operators.
ADAMS offers operators for machine learning libraries like WEKA and MOA and image processing libraries such as ImageJ, Java Advanced Imaging (JAI), ImageMagick and Gnuplot. Via Rserve, the R-Project can be incorporated in flows for data processing. With the WEKA webservice, other frameworks can take advantage of WEKA's models as well. For fast prototyping the user can use scripting languages such as Groovy and Jython.

File commander -- dual-pane file manager (inspired by Norton/Midnight commander)
that allows you to manage local and remote files (ftp, sftp, smb); usually faster
than native file managers (like Windows Explorer, Nautilus, Caja) in terms of
handling 10s of thousand of files in a single directory

experimental deeplearning4j module

module for querying/consuming webservices using Groovy

basic terminal-based GUI for remote machines (eg cloud)

many interactive actors can be used in headless environment now as well

Fixed a memory leak introduced by Java's logging framework

Flow editor now has predefined rules for swapping actors, e.g. Trigger
with Tee or ConditionalTrigger, maintaining as many options as possible
(including any sub-actors).